Interested in this AI/ML Engineer role at Intuitive (Intuitive Surgical)?
Apply Now →About This Role
Sunnyvale, CA, United States
Not Remote
Engineering
JOB215609
Company Description
It started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later, that question still fuels everything we do at Intuitive. As a global leader in robotic\-assisted surgery and minimally invasive care, our technologies—like the da Vinci surgical system and Ion—have transformed how care is delivered for millions of patients worldwide.
We’re a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human. Every day, our work helps care teams perform with greater precision and patients recover faster, improving outcomes around the world.
The problems we solve demand creativity, rigor, and collaboration. The work is challenging, but deeply meaningful—because every improvement we make has the potential to change a life.
If you’re ready to contribute to something bigger than yourself and help transform the future of healthcare, you’ll find your purpose here.
Job Description
We are seeking a Senior Director, AI Product \& Strategy to define and drive the company’s enterprise AI agenda. This leader will operate at the intersection of business strategy, product management, and applied AI, translating a rapidly evolving technology landscape into clear, actionable business outcomes.
This is a highly visible, externally aware role that partners closely with enterprise business leaders and business\-facing teams to identify, shape, and prioritize AI\-driven opportunities. A core responsibility of this role is to bridge the gap between business intent and technical execution—ensuring that AI initiatives are well\-defined, feasible, and positioned to deliver measurable impact.
In addition, this role will play a key part in building AI product management capabilities in the organization, elevating how business needs are translated into scalable AI solutions.
Key Responsibilities
1\. Enterprise AI Strategy \& External Point of View
- Partner with the VP, Data, Analytics \& Enterprise AI to define and evolve the enterprise AI strategy aligned with company priorities
- Develop a forward\-looking point of view on AI, including generative AI, agentic systems, automation, and advanced analytics
- Stay current on rapidly emerging technologies, tools, and market trends, and translate them into clear strategic direction for the company
- Provide thought leadership to executive stakeholders, synthesizing complex developments into actionable insights
2\. Business Engagement \& Use Case Identification
- Serve as a strategic partner to enterprise business leaders across Enterprise Business Functions.
- Lead structured conversations to:
o Identify manual, inefficient, and high\-friction workflows
o Reimagine them using AI, automation, and advanced analytics
- Collaborate with business\-facing teams in Data \& Analytics and Business
Applications to:
o Articulate the art of the possible
o Bring credibility to conversations, having had the experience building and deploying Ai solutions to production.
o Identify and define high\-impact AI use cases
- Build and maintain a pipeline of prioritized AI opportunities aligned to measurable business value
3\. AI Product Translation \& Requirements Definition
- Lead the translation of business\-level problem statements into clear AI product and solution definitions
- Work closely with business\-facing teams to:
o Convert use cases into structured requirements, user journeys, and success metrics
o Define data needs, model expectations, and operational workflows
- Partner with AI Engineering and Data Science teams to ensure:
o Requirements are feasible, well\-scoped, and aligned with technical realities
o Solutions are designed for scalability, maintainability, and enterprise integration
- Act as the bridge between business stakeholders / business facing portfolio teams and technical teams, ensuring clarity, alignment, and shared ownership
4\. AI Product Strategy \& Portfolio Development
- Define and guide the enterprise AI product and use case portfolio, ensuring alignment with strategic priorities
- Establish frameworks for use case prioritization, balancing business impact, feasibility, and scalability
- Drive clarity on:
o What should be built
o Why it matters
o How success will be measured
- Ensure AI initiatives are delivered as products (not one\-off projects) with clear ownership, lifecycle management, and value tracking
5\. Executive Communication \& Influence
- Communicate AI strategy, priorities, and progress to senior leadership and the executive committee with clarity and conviction
- Build alignment by articulating:
o Business value and ROI
o Trade\-offs, risks, and dependencies
- Distinguish between high\-impact opportunities and low\-value or overhyped initiatives
- Serve as a trusted advisor to leadership on AI investments and direction
6\. Technical Credibility \& Solution Shaping
- Bring depth in AI/ML to:
o Evaluate solution approaches and architectures
o Challenge assumptions and guide teams toward practical, high\-impact implementations
- Ensure alignment with enterprise data strategy, architecture, and governance
- Help define guardrails for responsible, ethical, and secure use of AI
7\. AI Product Capability Building
- Build and develop AI product management capabilities within business\-facing teams.
- Establish best practices for:
o Translating business needs into AI requirements
o Structuring use cases and defining success metrics
o Managing AI initiatives as products
- Coach and mentor teams to improve their ability to:
o Engage effectively with Data Science and Engineering
o Drive clarity and discipline in AI solution definition
Qualifications
Experience
- 12\+ years of experience in product management, strategy, or enterprise technology leadership, with a strong focus on data and AI
- Bachelor's degree in a relevant field (Computer Science, Information Systems, Business, Law, Data Management, or equivalent). Advanced degree preferred
- Hands\-on experience with building and deploying AI products to production.
- Proven experience working with enterprise business functions to drive transformation through technology
- Demonstrated success in translating business problems into scalable AI product/ technology solutions
- Experience influencing executive stakeholders and shaping enterprise strategy
AI / Technical Acumen
- Strong understanding of:
o Machine learning and advanced analytics
o Generative AI (LLMs, copilots, agent\-based systems)
o Enterprise data platforms and AI architectures
- Ability to:
o Translate business needs into clear AI solution requirements
o Distinguish between feasible, scalable solutions and experimental or low\- impact ideas
- Comfortable engaging deeply with Data Science and Engineering teams without being hands\-on
Leadership \& Communication
- Exceptional ability to translate complex AI concepts into clear, business\-relevant language
- Strong executive presence with the ability to influence and align senior stakeholders
- Demonstrated ability to operate in ambiguous, fast\-moving environments and drive clarity
- Proven ability to lead cross\-functional initiatives and build consensus across diverse teams
Additional Information
Due to the nature of our business and the role, please note that Intuitive and/or your customer(s) may require that you show current proof of vaccination against certain diseases including COVID\-19\. Details can vary by role.
Intuitive is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified applicants and employees, and prohibit discrimination and harassment of any type, without regard to race, sex, pregnancy, sexual orientation, gender identity, national origin, color, age, religion, protected veteran or disability status, genetic information or any other status protected under federal, state, or local applicable laws.
Mandatory Notices
U.S. Export Controls Disclaimer: In accordance with the U.S. Export Administration Regulations (15 CFR §743\.13(b)), some roles at Intuitive Surgical may be subject to U.S. export controls for prospective employees who are nationals from countries currently on embargo or sanctions status.
Certain information you provide as part of the application will be used for purposes of determining whether Intuitive Surgical will need to (i) obtain an export license from the U.S. Government on your behalf (note: the government’s licensing process can take 3 to 6\+ months) or (ii) implement a Technology Control Plan (“TCP”) (note: typically adds 2 weeks to the hiring process).
For any Intuitive role subject to export controls, final offers are contingent upon obtaining an approved export license and/or an executed TCP prior to the prospective employee’s start date, which may or may not be flexible, and within a timeframe that does not unreasonably impede the hiring need. If applicable, candidates will be notified and instructed on any requirements for these purposes.
We will consider for employment qualified applicants with arrest and conviction records in accordance with fair chance laws.
Preference will be given to qualified candidates who do not reside, or plan to reside, in Alabama, Arkansas, Delaware, Florida, Indiana, Iowa, Louisiana, Maryland, Mississippi, Missouri, Oklahoma, Pennsylvania, South Carolina, or Tennessee.
This position may be filled at a different job level than listed here depending on
business need and/or on the selected candidate’s experience, knowledge and skills.
Compensation will be based primarily on the job level at which the role is filled and the
candidate’s qualifications, consistent with applicable law.
We provide market\-competitive compensation packages, inclusive of base pay, incentives, benefits, and equity. It would not be typical for someone to be hired at the top end of range for the role, as actual pay will be determined based on several factors, including experience, skills, and qualifications. The target compensation ranges are listed.
Base Compensation Range Region 1: $277,600 USD \- $416,400 USD
Base Compensation Range Region 2: $236,000 USD \- $353,900 USD
Shift: Day
Workplace Type: Set Schedule \- This job will be onsite weekly, the percentage of onsite work will be defined by the leader.
Salary Context
This $236K-$416K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Intuitive (Intuitive Surgical), this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($326K) sits 82% above the category median. Disclosed range: $236K to $416K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Intuitive (Intuitive Surgical) AI Hiring
Intuitive (Intuitive Surgical) has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sunnyvale, CA, US. Compensation range: $416K - $416K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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